Pattern recognition for tasks such as automatic speech recognition has been an active field of research. One well established framework for a variety of pattern recognition applications is based on pattern models known as Hidden Markov Models (HMMs), which provide state space models with latent variables describing interconnected states where each state is represented by a Gaussian distribution or by a mixture of Gaussians, for modeling data with sequential patterns. Units of a speech signal, such as phones, may be associated with one or more states of the pattern models. Typically, the pattern models incorporate classification parameters that must be trained to identify the pattern that correctly matches an input signal, such as by revising the means and variances of the distributions for the pattern models. One technique for training HMM parameters is to use discriminative training, in which the goal is to set the HMM parameters so that the HMM is able to discriminate between a correct pattern and one or more relatively similar but incorrect patterns.
One specific form of discriminative training is known as minimum classification error (MCE) training. In MCE training, the HMM parameters are trained by optimizing an objective function that is closely related to classification errors, where a classification error is the selection of an incorrect word sequence instead of a correct word sequence. Another form of discriminative training is known as maximization of mutual information (MMI). Under MMI, an objective function related to the mutual information is optimized using one of a set of optimization techniques. One of these techniques is known as Growth Transformation (GT) or Extended Baum-Welch (EBW). However, it remains a challenge to effectively model distinguishing characteristics of a correct target pattern from the many possible erroneous competing possibilities.
The discussion above is merely provided for general background information and is not intended to be used as an aid in determining the scope of the claimed subject matter.